The current AI in education is just a warm-up. From my talks with researchers and founders in the Valley, the whole game is about to change. Here are my 3 big predictions for 2027 that will change what 'personalized learning' means.
Why Most Approaches Fail
Many common approaches to AI in education are flawed at the root.
The root cause is usually one of three things:
Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
The Framework That Actually Works
Here is a simple framework for evaluating AI in education. It's not complicated, but it requires discipline.
Step 1: the best solutions are often the simplest ones
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: your team matters more than your technology
Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly
Nothing works perfectly the first time. The companies that get AI in education right treat it as an ongoing process, not a one-time project.
The Counterintuitive Truth
In AI in education, the best practitioners often do less, not more.
The lesson: your team matters more than your technology. It sounds simple. It's incredibly hard to execute.
Real Talk: What Actually Matters
Here is what actually matters in AI in education.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on AI in education. Many fail because they moved too slowly.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. It sounds obvious, but many founders make decisions about AI in education in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around personalized learning, ai trends, future of edtech, predictions that I've been thinking about a lot lately.
What's Next
Best practice in AI in education is changing fast. What worked last year might not work next year. That's both the challenge and the opportunity.
My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.
If you're building in this space, the best time to get AI in education right is before you need to. Don't wait for a crisis to force your hand.
I'll keep sharing what I learn. This stuff matters too much to keep to myself.
Frequently Asked Questions
Q:Which item on this list has the highest impact?
It depends on your stage and context, but in my experience, the items near the top of the list tend to have the broadest applicability. That said, sometimes the less obvious items create the biggest breakthroughs for specific situations.
Q:How were these items selected?
Each item on this list comes from direct experience, either from building my own companies or from patterns I've observed across the 234 startups I've invested in. I prioritize practical, actionable items over theoretical concepts.
Q:Are these recommendations still relevant in 2026?
Absolutely. While specific tools and tactics change, the underlying principles remain consistent. I update my thinking regularly based on what I'm seeing in the market and across my portfolio companies.